Boolean models of within-host immune interactions.

The role of various immune cells and intra-cellular components involved in immune responses has been elucidated. We describe how this information can be assembled in the form of causal interaction networks and how the dynamics of these networks can be described by qualitative/semi-qualitative modeling methods even in the absence of knowledge about kinetic constants. Recent models analyze signaling induced by the epidermal growth factor, the stimuli leading to pathological conditions, pathogen induced cellular interactions, and the intra-cellular and cellular signaling involved in the regulation of T cell responses. The models make testable predictions regarding yet undetected interactions, process durations and strengths, and novel therapeutic targets, several of which have been experimentally validated.

[1]  Ilya Shmulevich,et al.  Eukaryotic cells are dynamically ordered or critical but not chaotic. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Carsten Peterson,et al.  Random Boolean network models and the yeast transcriptional network , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Madalena Chaves,et al.  Robustness and fragility of Boolean models for genetic regulatory networks. , 2005, Journal of theoretical biology.

[4]  R. Medzhitov.  Recognition of microorganisms and activation of the immune response , 2007, Nature.

[5]  C. Espinosa-Soto,et al.  A Gene Regulatory Network Model for Cell-Fate Determination during Arabidopsis thaliana Flower Development That Is Robust and Recovers Experimental Gene Expression Profilesw⃞ , 2004, The Plant Cell Online.

[6]  D. Thieffry,et al.  A logical analysis of the Drosophila gap-gene system. , 2001, Journal of theoretical biology.

[7]  R. Antia,et al.  The dynamics of acute malaria infections. I. Effect of the parasite's red blood cell preference , 2008, Proceedings of the Royal Society B: Biological Sciences.

[8]  Holger Fröhlich,et al.  Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance , 2009, BMC Systems Biology.

[9]  Stefan Bornholdt,et al.  Topology of biological networks and reliability of information processing , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[10]  S. Huang,et al.  Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks. , 2000, Experimental cell research.

[11]  Song Li,et al.  Boolean network simulations for life scientists , 2008, Source Code for Biology and Medicine.

[12]  David Zhang,et al.  The crosstalk between EGF, IGF, and Insulin cell signaling pathways - computational and experimental analysis , 2009, BMC Systems Biology.

[13]  A. Bauer-Mehren,et al.  Pathway databases and tools for their exploitation: benefits, current limitations and challenges , 2009, Molecular systems biology.

[14]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[15]  Réka Albert,et al.  Modeling Systems-Level Regulation of Host Immune Responses , 2007, PLoS Comput. Biol..

[16]  Steffen Klamt,et al.  Host-pathogen systems biology: logical modelling of hepatocyte growth factor and Helicobacter pylori induced c-Met signal transduction , 2008, BMC Systems Biology.

[17]  José A. M. Borghans,et al.  Sparse production but preferential incorporation of recently produced naïve T cells in the human peripheral pool , 2008, Proceedings of the National Academy of Sciences.

[18]  R. Albert,et al.  Systems-level insights into cellular regulation: inferring, analysing, and modelling intracellular networks. , 2007, IET systems biology.

[19]  Holger Conzelmann,et al.  Dynamic pathway modeling of signal transduction networks: a domain-oriented approach. , 2008, Methods in molecular biology.

[20]  Rustom Antia,et al.  Estimation of the rate of killing by cytotoxic T lymphocytes in vivo , 2007, Proceedings of the National Academy of Sciences.

[21]  R. Albert,et al.  Network model of survival signaling in large granular lymphocyte leukemia , 2008, Proceedings of the National Academy of Sciences.

[22]  Ioannis Xenarios,et al.  A method for the generation of standardized qualitative dynamical systems of regulatory networks , 2005, Theoretical Biology and Medical Modelling.

[23]  Denis Thieffry,et al.  Segmenting the fly embryo: a logical analysis of the pair-rule cross-regulatory module. , 2003, Journal of theoretical biology.

[24]  L. Glass,et al.  The logical analysis of continuous, non-linear biochemical control networks. , 1973, Journal of theoretical biology.

[25]  Julio Saez-Rodriguez,et al.  Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling , 2007, PLoS Comput. Biol..

[26]  Steffen Klamt,et al.  Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling , 2009, BMC Systems Biology.

[27]  Réka Albert,et al.  Constraint-based network model of pathogen–immune system interactions , 2009, Journal of The Royal Society Interface.